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README.md

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+---
+license: cc-by-nc-4.0
+language:
+- sin
+- pli
+- san
+tags:
+- language-identification
+- fasttext
+- continual-learning
+- leaf-surgery
+- table-2-target-only
+- sinhala
+- pali
+- sanskrit
+pipeline_tag: text-classification
+base_model: facebook/fasttext-language-identification
+---
+
+# fastText Continual Leaf Surgery: Target-Only Training (Table 2)
+
+This model represents our **Continual Learning (Leaf Surgery)** architecture trained strictly on the target dataset (Table 2 in the research paper):
+> **"A Benchmark for Sinhala-Script Language Identification: Disambiguating Sinhala, Pali, and Sanskrit in Closed and Open World Contexts"**  
+> *University of Moratuwa, Department of Computer Science & Engineering*
+
+## 1. Architecture & Innovation
+Instead of fine-tuning with a replacement flat softmax head (which ruins fastText's Huffman tree and causes catastrophic forgetting), our method surgically splits the Sinhala (`si`) leaf node in the binary Huffman tree into modern Sinhala and canonical Pali branches.
+
+All original 176 language decision nodes are retained intact, and the model is trained strictly on target text (`train.csv`).
+
+## 2. Checkpoint Files
+- `weights.pt`: PyTorch weights containing inherited input embedding and expanded output hierarchical decision parameters (124.5 MB).
+- `config.json`: Tree paths, Huffman bit codes, and label mapping.
+- `vocab.json`: Subword vocabulary and hashes.
+
+## 3. How to Load and Run Inference in Python
+```python
+from data_pipeline.fasttext_continual.model import ContinualLID
+
+# Loads weights.pt, config.json, and vocab.json automatically from Hugging Face
+model = ContinualLID.from_pretrained("script-langid/fasttext-leaf-surgery-target-only")
+
+# Inference example:
+text = "මනොපුබ්බඞ්ගමා ධම්මා මනොසෙට්ඨා මනොමයා"
+prediction, score = model.predict(text)
+print(f"Language: {prediction}, Score: {score:.4f}")
+# Output: Language: pi, Score: 0.99...
+```